• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.633-638
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• 2006

We investigated the importance and efficiency of active and passive exploration on the recognition of objects in a variety of virtual environments (VEs). In this study, 54 participants (19 males and 35 females) were randomly allocated into one of two navigation conditions (active and passive navigation). The 3D visual display was presented through HMD and participants used joysticks to navigate VEs. The VEs consisted of exploring four rooms (library, office, lounge, and conference room), each of which had 15 objects. 'Active navigation' was performed by allowing participants to self-pace and control their own navigation within a predetermined time limitation for each room. 'Passive navigation' was conducted by forced navigation of the four rooms in random order. Total navigation duration and objects for both navigations were identical. After navigating VEs, participants were asked to recognize the objects that had been in the four rooms. Recognition for objects was measured by response time and the percentage of correct, false, hit, and miss responses. Those in the active navigation condition had a significantly higher percentage of hit responses (t (52) = 4.000 p < 0.01), and a significantly lower percentage of miss responses (t (52) = -3.763, p < 0.01) in object recognition than those in the passive condition. These results suggest that active navigation plays an important role in spatial cognition as well as providing a better explanation about the efficiency of learning in a 3D-based program.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.338-343
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• 1999

In this paper, the fuzzy neural network for the obstacle avoidance, which consists of the straight-line navigation and the barrier elusion navigation, is proposed and examined. For the straight-line navigation, the fuzzy neural network gets two inputs, angle and distance between the line and the mobile robot, and produces one output, steering velocity of the mobile robot. For the barrier elusion navigation, four ultrasonic sensors measure the distance between the barrier and the mobile robot and provide the distance information to the network. Then the network outputs the steering velocity to navigate along the obstacle boundary. Training of the proposed fuzzy neural network is executed in a given environment in real-time. The weights adjusting uses the back-propagation of the gradient of error to be minimized. Computer simulations are carried out to examine the efficiency of the real time learning and the guiding ability of the proposed fuzzy neural network. It has been shown that the mobile robot that employs the proposed fuzzy neural network navigates more safely with and less trembling locus compared with the previous reported efforts.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.713-716
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• 1991

This paper describes a methodology of mobile robot navigation which is designed to carry heavy payloads at high speeds to be used in FMS(Flexible Manufacturing System) without human control. Intelligent control scheme using fuzzy logic is applied to the navigation control. It analyzes sensor readings from multi-sensor system, which is composed of ultrasonic sensors, infrared sensors and odometer, for environment learning, planning, landmark detecting and system control. And it is implemented on a physical robot, AGV(Autonomous Guided Vehicle) which is a two-wheeled, indoor robot. An on-board control software is composed of two subsystems, i.e., AGV control subsystem and Sensor control subsystem. The results show that the navigation of the AGV is robust and flexible, and a real-time control is possible.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.1-8
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• 2002

It has been integrated into several navigation systems. This paper shows that system recognizes difficult indoor roads without any specific marks such as painted guide line or tape. In this method the robot navigates with visual sensors, which uses visual information to navigate itself along the read. The Neural Network System was used to learn driving pattern and decide where to move. In this paper, I will present a vision-based process for AMR(Autonomous Mobile Robot) that is able to navigate on the indoor read with simple computation. We used a single USB-type web camera to construct smaller and cheaper navigation system instead of expensive CCD camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.293-298
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• 2006

In this paper, a sensor fusion based robot navigation method for the autonomous control of a miniature human interaction robot is presented. The method of navigation blends the optimality of the Fuzzy Neural Network(FNN) based control algorithm with the capabilities in expressing knowledge and learning of the networked Intelligent Robotic Space(IRS). States of robot and IR space, for examples, the distance between the mobile robot and obstacles and the velocity of mobile robot, are used as the inputs of fuzzy logic controller. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a sensor fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process. Preliminary experiment and results are shown to demonstrate the merit of the introduced navigation control algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.168-169
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• 2022

In this study, we investigates the auto-berthing problem for the underactuated surface vessel in the presence of constraints of dynamic uncertainties, finite time, transmission load, and environmental disturbance. A novel control scheme is proposed by fusing the finite time control technology and the event-triggered input algorithm. In the algorithm, differential homeomorphism coordinate the transformation is used to solve the problem of underactuation. Then, we apply the finite time technology and event triggered to save the time of the berthing vessel and relieve transmission burden between the controller and the vessel respectively. Moreover, a radial basis function network is used to approximate unknown nonlinear functions, and minimum learning parameters are introduced to lessen the computational complexity. A sufficient effort has been made to verify the stability of the closed-loop system based on the Lyapunov stability theory. Finally, simulation results display the effectiveness of the proposed scheme.

• Journal of Navigation and Port Research
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• v.35 no.9
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• pp.721-727
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• 2011

This paper compares the English language programs of two maritime institutes in very different parts of the world, South Korea and Belgium. It will show that although both institutes comply with the minimum standard set by the STCW Code issued by the IMO, course contents and requirements can vary greatly. Method of class delivery, choice of learning tools and testing will be examined to illustrate some of these differences. This paper will show that in order to train new seafarers in the best possible way that will fulfill the IMO mandate of Safer Shipping and Cleaner Oceans, continual review and development of course curriculum should be an ongoing process that focusing on encouraging cadets to communicate in a targeted, efficient and professional way within a nautical context. This paper will show by comparison the huge potential that the Korean Maritime English program has for positive change and growth.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.19-20
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• 2011

This paper compares the English language programs of two maritime institutes in very different parts of the world, South Korea and Belgium. It will show that although both institutes comply with the minimum standard set by the STCW Code issued by the IMO, requirements for entry and graduation can vary greatly. Method of class delivery, choice of learning tools and testing systems will also be examined to illustrate these differences. It will also shown that in order to train new seafarers in the best possible ways that will fulfill the IMO mandate of Safer Shipping and Cleaner Oceans, examination of course curriculum at any maritime institute should be an ongoing process so that cadets are encouraged to communicate in a targeted, efficient and professional way within a nautical context.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2337-2341
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• 2005

This paper presents the target detection method using Support Vector Machines(SVMs) and the navigation system using behavior-based fuzzy controller. SVM is a machine-learning method based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate detection of target objects as a supervised-learning problem and apply SVM to detect at each location in the image whether a target object is present or not. The behavior-based fuzzy controller is implemented as an individual priority behavior: the highest level behavior is target-seeking, the middle level behavior is obstacle-avoidance, the lowest level is an emergency behavior. We have implemented and tested the proposed method in our mobile robot "Pioneer2-AT". Comparing with a neural-network based detection method, a SVM illustrate the excellence of the proposed method.

• Journal of the Korean Institute of Navigation
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• v.22 no.2
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• pp.47-52
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• 1998

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.